553,102 research outputs found

    Efficient Portfolio Selection

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    Merak believed that an efficient frontier analysis method that combined the robustness of the Monte Carlo approach with the confidence of the Markowitz approach would be a very powerful tool for any industry. However, it soon became clear that there are other ways to address the problem that do not require a Monte Carlo component. Three subgroups were formed, and each developed a different approach for solving the problem. These were the Portfolio Selection Algorithm Approach, the Statistical Inference Approach, and the Integer Programming Approach

    Selective inference after feature selection via multiscale bootstrap

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    It is common to show the confidence intervals or pp-values of selected features, or predictor variables in regression, but they often involve selection bias. The selective inference approach solves this bias by conditioning on the selection event. Most existing studies of selective inference consider a specific algorithm, such as Lasso, for feature selection, and thus they have difficulties in handling more complicated algorithms. Moreover, existing studies often consider unnecessarily restrictive events, leading to over-conditioning and lower statistical power. Our novel and widely-applicable resampling method addresses these issues to compute an approximately unbiased selective pp-value for the selected features. We prove that the pp-value computed by our resampling method is more accurate and more powerful than existing methods, while the computational cost is the same order as the classical bootstrap method. Numerical experiments demonstrate that our algorithm works well even for more complicated feature selection methods such as non-convex regularization.Comment: The title has changed (The previous title is "Selective inference after variable selection via multiscale bootstrap"). 23 pages, 11 figure

    Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner

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    Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) meta-heuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process, while ?Ant-Miner is an extension of the algorithm that selects the rule class prior to its construction, and utilizes multiple pheromone types, one for each permitted rule class. In this paper, we combine these two algorithms to derive a new approach for learning classification rules using ACO. The proposed approach is based on using the measure function for 1) computing the heuristics for rule term selection, 2) a criteria for discretizing continuous attributes, and 3) evaluating the quality of the constructed rule for pheromone update as well. We explore the effect of using different measure functions for on the output model in terms of predictive accuracy and model size. Empirical evaluations found that hypothesis of different functions produce different results are acceptable according to Friedman’s statistical test
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